Voice recognition systems require a high quality input voice (speech) signal to work properly. Similarly, the remote party of an in-vehicle mobile phone conversation will best understand what is being said if the input voice signal contains a minimum of extraneous sound. In either case, the quality of the input signal can be compromised by sound sources other than the input voice signal (i.e., background noise) that are active during the listening period of a phone conversation, or the voice recognition period of a speech recognition system. The result can be poor communication, false recognition, or no recognition at all. This problem is known to happen in automobiles where tire, wind, or engine noise is present while driving. Background noise that is constant in amplitude and frequency range can be mitigated somewhat using sound dampening, active noise cancellation (ANC) systems, and the like. However, such measures can be ineffective when sudden changes in background noise amplitude and frequency occur, such as when passing or being passed by a loud vehicle, like a truck for example. Conventional ANC methods may be disrupted because their cancellation signal may be computed by generating a frequency and amplitude profile of the background noise over a certain amount of time, and the profile cannot accommodate amplitude or frequency changes that occur within a shorter amount of time. Alternatively, a phase-inverse signal based, for example, on a secondary microphone arranged to receive background noise may be combined with a primary voice microphone signal to mitigate the effect of external noise on the input signal. However, this method may have a limited effective frequency range, or may be adversely affected by a processing delay. Therefore these prior art methods are often not sufficient to prevent disruption to voice recognition systems or mobile phone conversations caused by the sudden appearance of loud external noise.